Despite the benefits, there are key challenges that limit the applications of FL over wireless mobile networks [1]}, [2]}, [3]}, [4]}. To date, FL has been applied in a wide variety of fields [5]}, [6]}, [7]}, [8]}. For example, mobile service providers such as Apple and Google investigate FL for enabling enhanced user experiences through learning from users' behavior [9]}, [10]}. However, in such important application scenarios, FL needs to deal with practical challenges [11]}. Specifically, FL usually involves a large number of mobile devices to ensure sufficient training data. In this case, it is challenging to deploy FL due to the limited bandwidth resources. In addition, the energy constraints of the individual devices also increase the difficulty of implementing FL. Although most existing spectrum allocation optimization methods mainly focus on total energy consumption, the energy cost of individual devices should be considered. Therefore, an effective spectrum allocation optimization method is critical to the adoption of FL in important applications.
